CONTROLLING THE SPREAD OF COVID-19 THROUGH OPTIMIZING THE EFFECTS OF VACCINATION AND TREATMENT

• Main Author: Sukma Rianti, Izmi
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/54976
• Institution: Institut Teknologi Bandung

Coronavirus Disease (COVID-19) is an infectious disease caused by the new coronavirus variant known as severe acute respiratory syndrome 2 (SARS-Cov-2). This virus was discovered for the first time in Wuhan Province, China, and has since spread widely and infected many people in various regions. Therefore, the transmission of COVID-19 must be halted to reduce the number of positive cases, increase the recovery rate of COVID-19 patients, and lower the death rate. In this study, the spread of the COVID-19 was modeled, and the effect of adding control elements in the form of vaccination and treatment of infected individuals will be evaluated. The COVID-19 spread model is a simple compartment model based on the Kermack-McKendrick Model. The behavior of each compartment will be explained using numerical simulations to provide an overview of the spread of COVID-19. It was discovered that the transmission rate had the greatest influence on the spread of infection. Constant control in the form of vaccine then given to the COVID-19 spread model to evaluate its effect. The numerical simulation results show that vaccination can reduce the number of infected individuals. The greater the number of people who are vaccinated, the more effective it will be in reducing the spread of infection. Treatment of infected individuals, in addition to vaccination, will be considered to reduce the spread of infection. To optimize the effects of vaccination and treatment, Optimal Control Theory will be used. This method can provide a better representation for small populations so it can be assumed that dealing with the spread of infection in a small population is beneficial than to dealing with it in a large population. Furthermore, the optimal control rate for a larger population is lower to reduce cost.